{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "sns.set(rc = {\"figure.figsize\":(10,8)}) \n",
    "sns.set(font_scale=1.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1245</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1026</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1111</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Attrition     BusinessTravel              Department  \\\n",
       "0   37          0      Travel_Rarely  Research & Development   \n",
       "1   54          0  Travel_Frequently  Research & Development   \n",
       "2   34          1  Travel_Frequently  Research & Development   \n",
       "3   39          0      Travel_Rarely  Research & Development   \n",
       "4   28          1  Travel_Frequently  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeNumber  \\\n",
       "0                 1          4  Life Sciences              77   \n",
       "1                 1          4  Life Sciences            1245   \n",
       "2                 7          3  Life Sciences             147   \n",
       "3                 1          1  Life Sciences            1026   \n",
       "4                 1          3        Medical            1111   \n",
       "\n",
       "   EnvironmentSatisfaction  Gender          ...           \\\n",
       "0                        1    Male          ...            \n",
       "1                        4  Female          ...            \n",
       "2                        1    Male          ...            \n",
       "3                        4  Female          ...            \n",
       "4                        1    Male          ...            \n",
       "\n",
       "   RelationshipSatisfaction  StandardHours StockOptionLevel  \\\n",
       "0                         3             80                1   \n",
       "1                         1             80                1   \n",
       "2                         4             80                0   \n",
       "3                         3             80                1   \n",
       "4                         1             80                2   \n",
       "\n",
       "   TotalWorkingYears TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  7                     2                4               7   \n",
       "1                 33                     2                1               5   \n",
       "2                  9                     3                3               9   \n",
       "3                 21                     3                3              21   \n",
       "4                  1                     2                3               1   \n",
       "\n",
       "  YearsInCurrentRole YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  5                       0                     7  \n",
       "1                  4                       1                     4  \n",
       "2                  7                       0                     6  \n",
       "3                  6                      11                     8  \n",
       "4                  0                       0                     0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data =pd.read_csv('pfm_train.csv')\n",
    "test_data =pd.read_csv('pfm_test.csv')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.999091</td>\n",
       "      <td>0.161818</td>\n",
       "      <td>9.427273</td>\n",
       "      <td>2.922727</td>\n",
       "      <td>1028.157273</td>\n",
       "      <td>2.725455</td>\n",
       "      <td>2.730909</td>\n",
       "      <td>2.054545</td>\n",
       "      <td>2.732727</td>\n",
       "      <td>6483.620909</td>\n",
       "      <td>...</td>\n",
       "      <td>2.696364</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.788182</td>\n",
       "      <td>11.221818</td>\n",
       "      <td>2.807273</td>\n",
       "      <td>2.746364</td>\n",
       "      <td>7.011818</td>\n",
       "      <td>4.207273</td>\n",
       "      <td>2.226364</td>\n",
       "      <td>4.123636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.037230</td>\n",
       "      <td>0.368451</td>\n",
       "      <td>8.196694</td>\n",
       "      <td>1.022242</td>\n",
       "      <td>598.915204</td>\n",
       "      <td>1.098053</td>\n",
       "      <td>0.706366</td>\n",
       "      <td>1.107805</td>\n",
       "      <td>1.109731</td>\n",
       "      <td>4715.293419</td>\n",
       "      <td>...</td>\n",
       "      <td>1.095356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.843347</td>\n",
       "      <td>7.825548</td>\n",
       "      <td>1.291514</td>\n",
       "      <td>0.701121</td>\n",
       "      <td>6.223093</td>\n",
       "      <td>3.618115</td>\n",
       "      <td>3.313830</td>\n",
       "      <td>3.597996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>504.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2924.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1026.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4857.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1556.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8354.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age    Attrition  DistanceFromHome    Education  \\\n",
       "count  1100.000000  1100.000000       1100.000000  1100.000000   \n",
       "mean     36.999091     0.161818          9.427273     2.922727   \n",
       "std       9.037230     0.368451          8.196694     1.022242   \n",
       "min      18.000000     0.000000          1.000000     1.000000   \n",
       "25%      30.000000     0.000000          2.000000     2.000000   \n",
       "50%      36.000000     0.000000          7.000000     3.000000   \n",
       "75%      43.000000     0.000000         15.000000     4.000000   \n",
       "max      60.000000     1.000000         29.000000     5.000000   \n",
       "\n",
       "       EmployeeNumber  EnvironmentSatisfaction  JobInvolvement     JobLevel  \\\n",
       "count     1100.000000              1100.000000     1100.000000  1100.000000   \n",
       "mean      1028.157273                 2.725455        2.730909     2.054545   \n",
       "std        598.915204                 1.098053        0.706366     1.107805   \n",
       "min          1.000000                 1.000000        1.000000     1.000000   \n",
       "25%        504.250000                 2.000000        2.000000     1.000000   \n",
       "50%       1026.500000                 3.000000        3.000000     2.000000   \n",
       "75%       1556.500000                 4.000000        3.000000     3.000000   \n",
       "max       2065.000000                 4.000000        4.000000     5.000000   \n",
       "\n",
       "       JobSatisfaction  MonthlyIncome          ...           \\\n",
       "count      1100.000000    1100.000000          ...            \n",
       "mean          2.732727    6483.620909          ...            \n",
       "std           1.109731    4715.293419          ...            \n",
       "min           1.000000    1009.000000          ...            \n",
       "25%           2.000000    2924.500000          ...            \n",
       "50%           3.000000    4857.000000          ...            \n",
       "75%           4.000000    8354.500000          ...            \n",
       "max           4.000000   19999.000000          ...            \n",
       "\n",
       "       RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
       "count               1100.000000         1100.0       1100.000000   \n",
       "mean                   2.696364           80.0          0.788182   \n",
       "std                    1.095356            0.0          0.843347   \n",
       "min                    1.000000           80.0          0.000000   \n",
       "25%                    2.000000           80.0          0.000000   \n",
       "50%                    3.000000           80.0          1.000000   \n",
       "75%                    4.000000           80.0          1.000000   \n",
       "max                    4.000000           80.0          3.000000   \n",
       "\n",
       "       TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "count        1100.000000            1100.000000      1100.000000   \n",
       "mean           11.221818               2.807273         2.746364   \n",
       "std             7.825548               1.291514         0.701121   \n",
       "min             0.000000               0.000000         1.000000   \n",
       "25%             6.000000               2.000000         2.000000   \n",
       "50%            10.000000               3.000000         3.000000   \n",
       "75%            15.000000               3.000000         3.000000   \n",
       "max            40.000000               6.000000         4.000000   \n",
       "\n",
       "       YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "count     1100.000000         1100.000000              1100.000000   \n",
       "mean         7.011818            4.207273                 2.226364   \n",
       "std          6.223093            3.618115                 3.313830   \n",
       "min          0.000000            0.000000                 0.000000   \n",
       "25%          3.000000            2.000000                 0.000000   \n",
       "50%          5.000000            3.000000                 1.000000   \n",
       "75%          9.000000            7.000000                 3.000000   \n",
       "max         37.000000           18.000000                15.000000   \n",
       "\n",
       "       YearsWithCurrManager  \n",
       "count           1100.000000  \n",
       "mean               4.123636  \n",
       "std                3.597996  \n",
       "min                0.000000  \n",
       "25%                2.000000  \n",
       "50%                3.000000  \n",
       "75%                7.000000  \n",
       "max               17.000000  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. StandardHours 方差是0，说明数据值全一样，drop\n",
    "2. EmployeeNumber 员工id，没有意义，drop\n",
    "3. Age 最小值18，所以Over18 可以drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(['Over18','StandardHours','EmployeeNumber'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4fe6320>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = train_data.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.heatmap(corrmat, vmax=0.8, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JobLevel、MothlyIncome、TotalWorkingYears想关性强；\n",
    "PercentSalaryHike、PerformanceRating相关性强。\n",
    "删除JobLevel、TotalWorkingYears、PerformanceRating特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(['JobLevel','TotalWorkingYears','PerformanceRating'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'Department', 'DistanceFromHome',\n",
       "       'Education', 'EducationField', 'EnvironmentSatisfaction', 'Gender',\n",
       "       'JobInvolvement', 'JobRole', 'JobSatisfaction', 'MaritalStatus',\n",
       "       'MonthlyIncome', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',\n",
       "       'RelationshipSatisfaction', 'StockOptionLevel', 'TrainingTimesLastYear',\n",
       "       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n",
       "       'YearsSinceLastPromotion', 'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5)) \n",
    "sns.countplot(x='BusinessTravel',hue='Attrition',data=train_data, ax=ax1)\n",
    "sns.barplot(x='BusinessTravel',y='Attrition',data=train_data,capsize=.05,ax=ax2)\n",
    "#显示BusinessTravel各个类型的离职率\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5)) \n",
    "sns.countplot(x='Department',hue='Attrition',data=train_data, ax=ax1)\n",
    "sns.barplot(x='Department',y='Attrition',data=train_data,capsize=.05,ax=ax2)\n",
    "#显示Department各个类型的离职率\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpKGqGat9nkgxh+/GiFjW+QMsAi4H5gLPAHsB/wzrNsA9Hfgq8AeKeX7nNCFWSZIkSRqSmrHa5xkUi7r05B6K/fx6Ou+LwBcHKi5JkiRJqpJmvPmTJEmSJJXM5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKqApm7xLkiRp8Fi5cgWrV6/uVjZy5Eja20eXFJGkTcHkT5IkSevccMPnmTfvLjo6OrqVt7UNY+rUQ5gx44SSIpO0sRz2KUmSpHXmzr3zRYkfQEfHWubOvbOEiCRtKiZ/kiRJWmfatENpa2t7UXlb2zCmTTu0hIgkbSoO+5QkSdI6M2acwBvf+BZOPfXPwzuvvPKzTJiwlXP+pEHON3+SJEnqpr29vdvxuHHjTfykIcDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKmBE2QFIklQlEbEfcA0wGbgPmJ6ZC+rabAF8BjgEWAPcCpyRmasiYjRwPXAYsBQ4NzNvbOJXkCQNUr75kySpSSKiHbgdmAlMAOYBs3poOhNoB3YBXg7sA5xRq7sYGAtsB7wVuCIiJg9o4JKkIcHkT5Kk5jkQWJyZN2fmKuAiYK+ImFLXrg24MDOXZeYi4GbgtbW6t9fqlmfmT4H/AGY0KX5J0iBm8idJUvNMAeZ3HmTmGmABsEfXRpl5fGb+vEvRocADETEB2KbrNYCsP1+SpJ4450+SpOYZCyyvK1sOjOnthIi4DNgdeGft/M5zGjq/04QJYxgxYni/glXfJk0aX3YIA2bUqLXdjrfeehxbbDF0v69UFSZ/kiQ1z3JgdF3ZGGBZfcOIGEGxMMyBwMGZ+UxEbFWrHg0819f59ZYsqc85tbEWLVpadggDZunS7n+lnnlmGatWOWCsGVauXMHq1au7lY0cOZL29vp/OqSe9fVgyuRPkqTmmQ8c23kQEcOBXSmGbtKlfDOKhWG2Bl6bmU8BZObiiFhEsVLozzqb158vaXC64YbPM2/eXXR0dHQrb2sbxtSphzBjxgklRaahwkc4kiQ1z/eAbSLimIgYBZwDLMjMh+rafZpiNdADOxO/Lm4BPhoR4yPi1cBRFIu+SBrk5s6980WJH0BHx1rmzr2zhIg01Jj8SZLUJJm5gmJ/vlOBZ4CpwJEAEfFgRLyjtsffu4FXAU9FxLLaz121y3y4du7vKd4OfiAz72/yV5E0AKZNO5S2thf/3/O2tjamTTu0hIg01DRl2GdEvJliX6KXAr8GTsvMH0TEocCVwA7Ad4EZtSWt6atOkqTBKjPvpdi3r758zy6Hva7MkpnPAe8agNAklWzGjBM46qijWbJkMR/4wMnryq+66lomTXpJiZFpqBjwN38RsQtwE/BeYEuKhG5ORLyUYpjKiRRzGp4EPlE7Z9ve6iRJUjV0rOm+6MXaF55nbV2ZNNS0t49m3LjxdWXtJUWjoaYZwz53BK7LzLszc21mfrlWfgxwT2Z+PzNXAmcBR0bE5sBb+6iTJElD3J8e+RFP//Kr3cqeeXA2i+7/En965EclRSVJg9uAJ3+1pO+MzuOIeA3FstS70n2j22eApbXy+k1wu9ZJkqQhbsWi+jVwOnX0USdJ6ktTt3qIiF2B2cBHKBK83ja6HQs83ktdr9zAVhp8hvImya3Oe69WNnrS7qxYNB+oX/mwjdGTppQRkiQNek1L/iJiX+AO4OrMvDwirqL3jW4b3gS3KzewlQafobxJcqsb7Pfe5HVo23zH1zJu+7+GjjXdK9qGM2z4yHKCkqRBrlmrfb4BuBX4YGZeXyueD7yhS5uJFAvC/HY9dZIkqQKKJM9ET5I2lWas9rkbcBvFVg3Xd6n6OvC6iJgaEe0UW0HMycxl66mTJEmSJPVTM1b7PJFiDt+NXTaqXQb8JfA24FPAU8D2wAkAmflYb3WSJEmSpP4b8GGftZU+z+ijyR69nPet3uokSZIkSf3TjDd/kiRJkqSSmfxJkiRJUgU0dZ8/SZIGs4jYFjgb2B3otrFsZh5USlCSJDXI5E+SpMbdAGxHsW/t6pJjkSSpX0z+JElq3GuA3TLzmbIDkSSpv5zzJ0lS4/4PWFV2EJIkbQjf/EmS1LiLgFkRcQnFPrTrZOYj5YQkSVJjTP4kSWrc52t/vrX2ZwfQVvtzeI9nSA04845zyw6hmzXPv9Dt+N/mXszwzVrn/zbOPPzCskOQBqXW+a9YkqTWt0vZAUiStKEamvMXEaf0Uv6RTRuOJEmtKzMfBhYDrwXeBvwdsLRWLklSS+v1zV9EbA3sWTu8LCJ+TjG0pdMWwIeACwYuPEmSWkdE7A7MA9YCDwM7A1dExEGZ+csyY5MkaX36Gva5CrgVeEnt+O4e6q8biKAkSWpRVwBfAM7LzI6IaKN4CPoJYFqpkUmStB69Jn+ZuRTYFiAi5mfmlKZFJUlSa9oHeEtmdgDUEsALgT+WG5YkSevX0Jw/Ez9JkgBYwZ9HxHR6CcX+f5IktbSGVvuszXG4DNiduqWsM/MvByAuSZJa0VeA2yPibIo5f7sAF1JMk5AkqaU1utXDNRRPOy8DVg9cOJIktbSPUPSJ3wBGASuBG4HzygxKkqRGNJr87Q1sn5nPDWQwkiS1ssxcCUyPiBOBCcAfO+f/SZLU6hpN/v4IjAVM/iRJlRMRZ2XmpRHxojd8EQFAZn6s6YFJktQPjSZ/nwG+FhGfBJ7qWpGZ/7XJo5IkqbX8HXApcGAv9R2AyZ8kqaU1mvx9qvbn/nXlHdQtACP15bSZc8oOoUefOvNNZYcgqYVl5qG1X/8xM5fU10fErk0OSZKkfmso+cvMhraEkCRpiHsY2LxrQUQMB+6rL5ckqdU0utXDjr3VZeYjmy4cSZJaS0TsAsyj2Bt3bET8rq7JWODRpgcmSVI/NTrscyHFEM+22nHnymZrKJa6HlQceihJalRm/j4i3kexmfvngI/WNXkeuLvpgUmS1E+NJn8vrTueBHwI+NamDUeSpNaTmXcCRMTvM9NETxpCfvLB95UdwousWLOm2/HPPvJhRg9vnWU29rniqrJD0AZqdM7fY3VFj0XE8cADwKxNHZQkSa0kIq7OzPdS7PF3bE9tMnNGk8OSJKlfGn3z15OtgC02VSCSJLWwtl5+77eI2A+4BphMsVDM9Mxc0EvbrWpt/j4zF9bK9gJ+Dqzo0vTYzJy9MXFJkoa+Rhd8uaGH8w4Gvr3JI5IkqcVk5ntqv94LfCEzn9uQ60REO3A7cAZwG3AWxQiaA3pouw9wA7BTXdXewB2Z+eYNiUGSVF2NbuHQVvezCvgkcNwAxSVJUiv6GLByI84/EFicmTdn5irgImCviJjStVFE7AHcAVzewzX2pnjzJ0lSvzQ65+9dAx2IJEmDwLeAD0TEFzPzjxtw/hRgfudBZq6JiAXAHl3LKbaOeFlmLouIWXXX2BsYFREPU6y+fW1mXrIBsUiSKqbRYZ9twOnA8cCOwJMUw1QuyMyOPk6VJGko+RvgbcBlEdGtIjMbWYpvLLC8rmw5MKbuWkv7uMYzFMNPr6EYEnpHRDyRmbP6+uAJE8YwYkTrrBY4FEyaNL7sECrLe18u7//g1eiCL2cA7wEuBn4P7Eqx1cMLtTJJkqrg6I08fzkwuq5sDLCs0Qtk5lFdDn8VEZ8F3sR6Vt9esqQ+59TGWrSorxxdA8l7Xy7vf2vrKzlvNPk7HnhTZv6ydvydiPgh8A36kfxFxOnAKzJzeu34NuAwis3iATIzX12rm04xt2ICxeT4EzNzRf01JUlqosMz88z6woi4jsY2ep8PrNsqIiKGUzxQzUY+PCJGU/SNF2bm/9WKN2Pj5iFKkiqi0eTvJcBDdWUPAVs2cnJEbAacC5wNfLFL1d7A/pn5s7r2rwRmAgcBC4Fbaud+pMF4JUnaJCJiO+D1tcOTI+IXdN/uYQvgXygelK7P94BtIuIYir7tLGBBZtb3sT3KzBUR8QZgWEScBQRwMi7AJklqQKPJ3wPADOC6LmUzgAcbPH8WML52fjtARIylmD/4qx7avx34SmY+UGt7HsWqZyZ/kqRmexp4L7ANxVu2j9XVPw9c0MiFasnbYRTz9T4L3A8cCRARDwIXZ+aX13OZI4Cra3H9qXbOXY19FUlSlTWa/J0NzI2Id1G8idsFeDlwSIPnn56ZT0TE+cDOtbJXAM8Bd0bEKyiWrT4lM+dTrIY2r8v5CWwbEVtl5uIGP1OSpI1W25LhNQAR8a3MfMNGXu9eYJ8eyvfspX1b3fFvgKkbE4MkqZoa3erhnoi4BNgBWAvcCXwtM/+7wfOf6KF4LPA/FIvJ/A74MDAnIvbkxauhdf4+Bug1+RvsK5m5clJ5vPfl8d6Xx3u/QZ7tqTAi5mbmtGYHI0lSfzS61cPZwEnAQZk5PyL+CfhURDyfmVdtyAdn5reBb3f5jPOB9wO78+LV0DqXwO5zNbTBvpKZKyeVx3tfHu99eQb7vW9W8hoROwLTa4dvrk1F6GoLYN+mBCNJGjArV65g9erV3cpGjhxJe3v9Is2DV6PDPk8CXlcbakJm3hYRvwTuAjYo+YuIw4FxmXlLrWh4LZ6VFKuhTe7aHPhjZvb4xFWSpAH0KMUm7NtQ9FMH1tWvBE5pdlCSpE3nhhs+z7x5d9HR0X0L87a2YUydeggzZpxQUmSbVqPJ35YUc/26+i3F084NNYri7eHPKYZ9Xgg8kJm/joivAN+MiBuB31BMrr95Iz5LkqQNkpkdFKt5EhHXZea6VT0j4iUUid8ngC+VE6EkaWPNnXtnj+UdHWuZO/fOIZP8DWuw3b3Av9aVvZ9ilbINkplfAy4H5gLPAHsB/1yruxc4Hfgq8AeKeX7nbOhnSZK0KXQmfhGxR0RcT/Fg9J3Ap8uMS5K0caZNO5S2thenRm1tbUybdmgJEQ2MRt/8fZBitc8TKYa/7ECx3HW/Jrdn5vl1xzMp9vPrqe0X6b4noCRJpYqI11M8nOzc9+/tFAugdfR+liSp1c2YcQJHHXU0S5Ys5gMfOHld+VVXXcukSS8pMbJNq9HVPu+LiMnA4cBfUCSAdzoHT5JUBbVN2U8HtqfYs/ZE4H+Be0z8JGloaG8fzbhx4+vK2kuKZmA0+uaP2v56Nw1gLJIktapZwLUU+9auAIiIUgOSJKm/Gk7+JEmqsDMoVr7+bURcS5EI+sZPkjSoNLrgiyRJlZWZn8jMycBxFHv6LQQmAgdERFuZsUmS1CiTP0mSGpSZd2Xm4cCeFPvcXgs8GhGXlhuZJEnrZ/InSVI/ZebvMvMMitWv/w2YWnJIkiStl3P+JEnaQJm5Evj32o8kSS3NN3+SJEnqZu0La7sfP7+GtavXlBSNpE3FN3+SJElaZ8n/Ps6yXy/uVvbknN9AG4zbbSsm7LtdSZFJ2li++ZMkSdI69YnfOh191EkaFEz+JEmStM64yVtBTxuYtNXqJA1aDvuUJEnSOhP23Y4tXrUNHWs7upW3DWtj2MjhJUUlaVMw+ZMkSVI3JnnS0OSwT0mSJEmqAJM/SZIkSaoAkz9JkiSpRaxau5aVa7vvqbh67dpeWkv945w/SZIkqQV859nF3P/csheVX//UE+w9dhwHb+lqq9o4vvmTJEmSWkBPiR9ARx91Un+Y/EmSJEkt4JVjx/W2xSKvHDuu2eFoCHLYpyRJktQCDt5yKw7YfEvWdHTfY3F4WxujhvnORhvP5E+SJElqESZ5Gkj+7ZIkSZKkCjD5kyRJkqQKMPmTJEmSpApwzp8kSZKkUlz3yW+WHUI3q1ev7Hb8xWu+y8iR7SVF82LHv/+QjTrfN3+SJEmSVAEmf5IkSZJUAQ77lCSpiSJiP+AaYDJwHzA9Mxf00narWpu/z8yFtbLRwPXAYcBS4NzMvLEJoUuSBjnf/EmS1CQR0Q7cDswEJgDzgFm9tN0HuBvYqa7qYmAssB3wVuCKiJg8QCFLkoYQkz9JkprnQGBxZt6cmauAi4C9ImJK10YRsQdwB3B5D9d4O3BhZi7PzJ8C/wHMGOC4JUlDgMmfJEnNMwWY33mQmWuABcAede0eBV5WP5wzIiYbWspUAAARwklEQVQA23S9BpA9nC9J0os450+SpOYZCyyvK1sOjOlakJlL+zi/85xez5ckqSdNTf4i4nTgFZk5vXZ8KHAlsAPwXWBGZi5aX50kSYPUcmB0XdkYYFk/zqd2jef6c/6ECWMYMWJ4gx+jRkyaNL7sECrLe18u7395NvbeNyX5i4jNgHOBs4Ev1sq2pZin8Gbgf4BPA58Aju6rrhnxSpI0QOYDx3YeRMRwYFeKoZvrlZmLI2IRxUqhP+u8TCPnL1lS/8JRG2vRot5e0Gqgee/L5f0vTyP3vq8EsVlz/mYBrwKu61L2VuCezPx+Zq4EzgKOjIjN11MnSdJg9T1gm4g4JiJGAecACzLzoX5c4xbgoxExPiJeDRxF8cBUkqQ+NSv5Oz0zDwee7FJWP+n9GYr9inZdT50kSYNSZq6g2J/vVOAZYCpwJEBEPBgR72jgMh+unft7im0jPpCZ9w9MxJKkoaQpwz4z84keiscCj9eVdU5a76uuV4N9PoPjp8vjvS+P97483vtyZOa9wD49lO/ZS/u2uuPngHcNTHSSpKGszNU++5r0vkET4gf7fAbHT5fHe18e7315Bvu9N3mVJKl/ytznbz7FhHUAImIisCXw2/XUSZIkSZL6qczk7+vA6yJiakS0AxcDczJz2XrqJEmSJEn9VFryl5mPAW8DPgU8BWwPnLC+OkmSJElS/zV1zl9mnl93/C1gj17a9lonSZIkSeqfMod9SpIkSZKaxORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJUuW9sGY1q19Y2a1szZrVJUUzMEaUHYAkSZIklSl/fw9/+OODLyr/wf03s8M2exC7HFBCVJueb/4kSZIkVVpPiV+ho4+6wcfkT5IkSVKl7bDNnkBbDzVttbqhwWGfkiRJkiotdjmAl+34GjrWrulW3jZsOCOGjywpqk3P5E+SJElS5Y0YPhKGUKLXE4d9SpIkSVIFmPxJkiRJUgWY/EmSJElSBZj8SZIkSVIFmPxJkiRJUgWY/EmSJElSBZj8SZIkSVIFmPxJkiRJUgWY/EmSJElSBZj8SZIkSVIFmPxJkiRJUgWY/EmSJElSBZj8SZIkSVIFjCg7AEmSqiQi9gOuASYD9wHTM3NBXZthwCeAo4E1wBWZeVmtbjzwLLCiyynnZeYnmhC+JGkQM/mTJKlJIqIduB04A7gNOAuYBRxQ1/RU4DXAbsBWwLyI+Glmfgd4BfDLzNy7WXFLkoaG0pO/iLgcOAV4oVa0MjMnRsShwJXADsB3gRmZuaikMCVJ2hQOBBZn5s0AEXER8IGImJKZ87u0OwqYmZmLgcUR8RngeOA7wN7Az5sctyRpCGiFOX97A2/PzHG1n4kRsS3wH8CJwNbAkxTDXyRJGsymAOuSvMxcAywA9uirHZBd2uwNTI6IjIjHIuKKiBg1gDFLkoaI0t/80fMTzLcC92Tm9wEi4izg8Yg4OTP/1OT4mubMO84tO4QezTz8wrJDkKShYiywvK5sOTBmPe26tlkGfB+4BNgCmA2cDZzf1wdPmDCGESOGb0jM6sWkSePLDqGyvPfl8v6XZ2PvfanJX0T8BcWbvSsjYn+Kp5/v58VPRp+JiKXArhST4yUNET70UMUsB0bXlY2hSOj6areuTWZ+sEv5/0XEpcA5rCf5W7KkPufUxlq0aGnZIVSW975c3v/yNHLv+0oQy37zN5Hi6eWlwL3AMcAdwDeAR+va9vRktBufag6MKjzdqcJ3VP9U4e9EFb5jC5oPHNt5EBHDKR5sZg/tJgMPdjbtbBMRHwVuzMzf1eo2A1YOYMySpCGi1OQvMx8ADu5SdH1EnAa8jmIYS1c9PRntxqeaA6MKT3eq8B3VP1X4OzHYv+MgTV6/B2wTEccAt1Cs9rkgMx+qa3cLcFZE/AAYT7Ew2mm1ulcBEREzgEm1a3yuGcFLkga3Uhd8iYi/iYj31BWPAj5J8cSzs91EYEvgt00MT5KkTSozVwCHUWzl8AwwFTgSICIejIh31JpeBdwD/AL4IXBNZs6p1R0PjAT+APwE+E+KfQMlSepT2cM+nwdmRsQDwP8A7wXaKd76fSwiplJ0fhcDczKzzzd/kiS1usy8F9inh/I9u/z+AsVegGf00O6PwBEDGaMkaWgq9c1fZv6UIuGbBfwf8Dbg8Mx8rPb7p4CngO2BE0oKU5IkSZIGvbLf/JGZNwE39VD+LV6875EkSZIkaQO0wibvkiRJkqQBZvInSZIkSRVg8idJkiRJFWDyJ0mSJEkVUPqCL5Kkcpx5x7llh9CjmYdfWHYIkiQNSb75kyRJkqQK8M2fhG9AJEmSNPT55k+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKmBE2QH0JSL2A64BJgP3AdMzc0G5UUmStOEa6dsiYhjwCeBoYA1wRWZetr46SZL60rJv/iKiHbgdmAlMAOYBs8qMSZKkjdGPvu1U4DXAbsD+wEkRcXADdZIk9aplkz/gQGBxZt6cmauAi4C9ImJKyXFJkrShGu3bjgIuz8zFmflb4DPA8Q3USZLUq1ZO/qYA8zsPMnMNsADYo7SIJEnaOI32bd3aAdmlTV91kiT1qpXn/I0FlteVLQfG9HbCpEnj2xq58M0ff8dGhDWQWjWuTcd7Xx7vfXm89+qi0b6tvl3XNn3V9aqRPrJ1/64OfbPe9amyQ6isQ2/6QtkhVNrZF/1z2SFUSiu/+VsOjK4rGwMsKyEWSZI2hUb7tvp2Xdv0VSdJUq9aOfmbT7ESGgARMRzYlWJ4iyRJg1GjfVu3dkB0adNXnSRJvWrlYZ/fA7aJiGOAW4CzgAWZ+VC5YUmStMEa7dtuAc6KiB8A44FTgNMaqJMkqVct++YvM1cAh1Esaf0MMBU4stSgJEnaCH31bRHxYER0Trq7CrgH+AXwQ+CazJzTQJ0kSb1q6+joKDsGSZIkSdIAa9k3f5IkSZKkTaeV5/xVTkScDrwiM6eXHUtVRMSbgYuBlwK/Bk7LzB+UG1U1RMQRFBtc7wA8BLwvM39UblTVEhF7APcBUzJzYcnhSL2yf2w++8dy2UeWayj3j775awERsVlEXADMLDuWKomIXYCbgPcCWwJXAnMiYnypgVVAROwM3Agcm5njgKuBW0sNqmIiYgTwBWCzsmORemP/WA77x3LZR5ZrqPePJn+tYRbwKuC6kuOomh2B6zLz7sxcm5lfrpVP7uskbbzaU7RtM/PHEbEFMJFi8Qs1z4eB/y47CGk9ZmH/WAb7xxLZR5ZuSPePDvtsDadn5hMRcT6wc8mxVEZm3g3c3XkcEa+h2Cz5N6UFVSGZuSwiXg7cD7wAHF5ySJUREXsDbwP2AU4vORypL/aPJbB/LJ99ZDmq0D/65q8FZOYTZcdQdRGxKzAb+Ehm/qnseCpkPtAOnATMjohJJccz5EXEKIrhLCfWth2QWpb9Y/nsH0tlH9lEVekfTf5UeRGxL8VeWddl5uVlx1Mlmbm69vMF4GHg78qOqQLOA77vwg2S1sf+sVz2kU1Xif7R5E+VFhFvAOYBZ2fm+SWHUxkR8fqImFdXPAp4tox4KuafgHdHxLMR0Xm/fxERR5UZlKTWYv9YHvvI0lSif3TOnyorInYDbgOmZ+bssuOpmPuBV0fEv1D8b3ASxb9HPyw1qgrIzCldjyOig2IJ/YXlRCSp1dg/ls4+sgRV6R9986cqOxEYC9wYEcu6/BxQdmBDXWY+DbwJ+BDwNPCPwKFDeYy9JA0i9o8lso/UQGrr6OgoOwZJkiRJ0gDzzZ8kSZIkVYDJnyRJkiRVgMmfJEmSJFWAyZ8kSZIkVYDJnyRJkiRVgMmfNAhExLCI2KnkGP6yzM+XJKkn9pFS49zkXepFRJwLvD4z/3497WYBZOb0Xuo3A84H3gZsAywH/gs4NzMfajCc/wAeAs6PiB2BXwF7ZOYj64nt/cC/1Q7378fn1V/nPcCRwIG14weBizPzyxtyPUnS4GYf2e069pEaNEz+pIH3aSCAgzJzYURsSdHR/VdE7JaZzzZwjW0oOjZqndm4Bj/7ZODSzLys/2G/6PPbOg8yc8+NvJ4kSWAfKTWVyZ/UgIg4ALgEeDmwCPg8cEVmrqk1mRQRc4ADgN8D/5qZ367V/S1wU2YuBMjMZyPiTGBzYFvg2Yj4C+CTwL4UnchjwEcz80sRcX3tuvtHxJ7AmbXP2KXWUZ4HHA+MBX4L/Ftm3hURfwC2Az4aEQdn5rSI+FfgaOClwBpgDnBSZj4fESOAjwDvrsV2L/DeWkxnA8Mj4snM3DYiFgLnZ+asiNgauAw4nGIo+d3ABzLzDxHx98As4N+B9wCbAd8GjsvMpRvzv4kkqTXYR9pHavBwzp+0HhERwDzgS8BE4K0U/0if3qXZGyj+8Z5E0UHdERE71+r+g2Ioyk0RMT0idgdeyMwZmTm/1uZ6iqEuewDjgc8B10bEuMw8DriHYgjJP9fFdiBwKvDazNyqdp2rI2JYZu4APELRcU2LiH8GTgPemplbAn8DvAl4R+1yH6n9PhXYCvgpcHtm3ghcDNyTmdv2cItmAztQdPovA5bVvn/nw6WdgL+o1f01RUd/Yu93XJI0WNhH2kdqcDH5k9bvHcD9mXlNZq7OzAco/qE/vkub/8zMr2fmC5l5E/Az4CiAzLyAojMcWTvvV8BjEfGBLuefSNFBPU/RSSwFxlB0MH15HtgCmBERr6To2P4yM9f20PabwL6Z+duI2ArYGni69nkAxwAfz8yHMvMFinkQx0ZEr/9O1Ca4vw54f2Yuqj2pPIWig963S9OPZuaKzPw98H1g9/V8L0nS4GAf2Qv7SLUikz9p/bYBFtaV/R7Yscvx7+rqHwa27zzIzLsy8+2ZuR2wC3AFcElEzKg12QWYSzFc5qsUnQWs57/RzPwh8E8UQ15+CDwBnNtLZzQMuDAiFgE/onjCuVmXz/iLWtyd116emT/upZPstE3tz4VdznsOeIou9ycz/9jlnNXA8L6+lyRp0LCP7J19pFqOyZ9UExFH18b7dxpJMczkYYqOp6uXAU92Od6+rn4XYGFE7B4RKyNi3fmZuTAzrwD+H/BXETESuAO4GZiYmfsCVzYY80uBxzNzKjCB4knrOcA/9ND8EmBn4GWZGZl5JMXT006PUsxz6Lz22Ii4IiIm9BFCZ0e47vtFxDiKDu/JHs+QJA069pH2kRoaXPBF+rPxwCkRcSPFRO83AndRzEc4NyJOopizMAU4C7ixy7lvjYhDgO8A02ttvkzxlPFe4MsR8b7a72OAg2o//0TxZHEM8FxmdtSWqf547boja392Dl2p99fAdbXJ6j+PiMdq5U/30HYCsBJ4ISLaKVY524tiQjsUk87PiIjvU3RY51F0kGf09vmZ+XhEfAu4MiKOrl3/KoqnnD+gmDMhSRr87CPtIzUE+OZP+rPrKSatPwgsAOYDl9XG4B9CMd7/aYqnkTcAH+ty7u0UTxOXUKwE9obMfDwzO4BDKf6Rv5niKeKTFKuRHZWZ38nMZcC7gPMiYinwXf48vKVzuegvUsxZ+HaXzyQzb6cYHjMnIp4DbgVOy8wf9/D9zqHoQJ+i6Lj2o+h8Oz/j48AtFCuNLQL2Bt5Y+w7fALaOiP+LiDF1130nRQf+C4ono1tS7P20uqebLEkalOwj7SM1BLR1dHSUHYMkSZIkaYD55k+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirA5E+SJEmSKsDkT5IkSZIqwORPkiRJkirg/wN6xYHaceEBJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5)) \n",
    "sns.countplot(x='JobSatisfaction',hue='Attrition',data=train_data, ax=ax1)\n",
    "sns.barplot(x='JobSatisfaction',y='Attrition',data=train_data,capsize=.05,ax=ax2) \n",
    "plt.show() \n",
    "#显示JobSatisfaction各个类型的离职率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5)) \n",
    "sns.countplot(x='Gender',hue='Attrition',data=train_data, ax=ax1)\n",
    "sns.barplot(x='Gender',y='Attrition',data=train_data,capsize=.05,ax=ax2) \n",
    "plt.show()  \n",
    "#显示Gender的离职率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(18,5)) \n",
    "sns.countplot(x='EducationField',hue='Attrition',data=train_data, ax=ax1)\n",
    "sns.barplot(x='EducationField',y='Attrition',data=train_data,capsize=.05,ax=ax2) \n",
    "plt.show()   \n",
    "#显示EducationField的离职率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(18,5)) \n",
    "sns.countplot(x='WorkLifeBalance',hue='Attrition',data=train_data, ax=ax1)\n",
    "sns.barplot(x='WorkLifeBalance',y='Attrition',data=train_data,capsize=.05,ax=ax2) \n",
    "plt.show()   \n",
    "#显示WorkLifeBalance的离职率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(18,5)) \n",
    "sns.pointplot(x='Attrition',y='JobSatisfaction',data=train_data,hue='Gender',capsize=.1,ax=ax1)\n",
    "sns.pointplot(x='Attrition',y='JobSatisfaction',data=train_data,hue='Department',capsize=.1,ax=ax2)\n",
    "##离职和不离职的人JobSatisfaction趋势.添加了一个部门观测值 \n",
    "plt.show()    \n",
    "##离职和不离职的人JobSatisfaction趋势.添加了一个性别观测值，可以看到女性对工作满意度普遍低于男性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc8385f8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x='Attrition',y='Age',data=train_data)\n",
    "##离职和不离职的人age趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertBool2Int(x):\n",
    "    if x == 'Y' or x == 'Yes'or x == 'Male':\n",
    "        return 1\n",
    "    elif x == 'N' or x == 'No'or x == 'Female':\n",
    "        return 0\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "##trans\n",
    "train_data[[ 'OverTime','Gender']] =train_data[[ 'OverTime','Gender']].applymap(str).applymap(convertBool2Int)  \n",
    "##onehot\n",
    "BusinessTravel = pd.get_dummies(train_data.BusinessTravel,prefix='bt') \n",
    "Department = pd.get_dummies(train_data.Department,prefix='d') \n",
    "EducationField = pd.get_dummies(train_data.EducationField,prefix='ef') \n",
    "JobRole= pd.get_dummies(train_data.JobRole,prefix='j') \n",
    "MaritalStatus= pd.get_dummies(train_data.MaritalStatus,prefix='ms') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>...</th>\n",
       "      <th>j_Laboratory Technician</th>\n",
       "      <th>j_Manager</th>\n",
       "      <th>j_Manufacturing Director</th>\n",
       "      <th>j_Research Director</th>\n",
       "      <th>j_Research Scientist</th>\n",
       "      <th>j_Sales Executive</th>\n",
       "      <th>j_Sales Representative</th>\n",
       "      <th>ms_Divorced</th>\n",
       "      <th>ms_Married</th>\n",
       "      <th>ms_Single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5993</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10502</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6074</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>12742</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2596</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  DistanceFromHome  Education  EnvironmentSatisfaction  Gender  \\\n",
       "0   37                 1          4                        1       1   \n",
       "1   54                 1          4                        4       0   \n",
       "2   34                 7          3                        1       1   \n",
       "3   39                 1          1                        4       0   \n",
       "4   28                 1          3                        1       1   \n",
       "\n",
       "   JobInvolvement  JobSatisfaction  MonthlyIncome  NumCompaniesWorked  \\\n",
       "0               2                3           5993                   1   \n",
       "1               3                3          10502                   7   \n",
       "2               1                3           6074                   1   \n",
       "3               2                4          12742                   1   \n",
       "4               2                2           2596                   1   \n",
       "\n",
       "   OverTime    ...      j_Laboratory Technician  j_Manager  \\\n",
       "0         0    ...                            0          0   \n",
       "1         0    ...                            0          0   \n",
       "2         1    ...                            1          0   \n",
       "3         0    ...                            0          0   \n",
       "4         0    ...                            1          0   \n",
       "\n",
       "   j_Manufacturing Director  j_Research Director  j_Research Scientist  \\\n",
       "0                         1                    0                     0   \n",
       "1                         1                    0                     0   \n",
       "2                         0                    0                     0   \n",
       "3                         1                    0                     0   \n",
       "4                         0                    0                     0   \n",
       "\n",
       "   j_Sales Executive  j_Sales Representative  ms_Divorced  ms_Married  \\\n",
       "0                  0                       0            1           0   \n",
       "1                  0                       0            1           0   \n",
       "2                  0                       0            0           0   \n",
       "3                  0                       0            0           1   \n",
       "4                  0                       0            1           0   \n",
       "\n",
       "   ms_Single  \n",
       "0          0  \n",
       "1          0  \n",
       "2          1  \n",
       "3          0  \n",
       "4          0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \n",
    "features=['Age',  'DistanceFromHome',\n",
    "       'Education',  'EnvironmentSatisfaction', 'Gender',\n",
    "       'JobInvolvement', 'JobSatisfaction', \n",
    "       'MonthlyIncome', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',\n",
    "       'RelationshipSatisfaction', 'StockOptionLevel', 'TrainingTimesLastYear',\n",
    "       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n",
    "       'YearsSinceLastPromotion', 'YearsWithCurrManager']\n",
    "trainData = pd.concat([train_data[features],BusinessTravel,Department, EducationField, JobRole,MaritalStatus], axis = 1)\n",
    "\n",
    "target =train_data['Attrition'] \n",
    "trainData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'DistanceFromHome', 'Education', 'EnvironmentSatisfaction',\n",
       "       'Gender', 'JobInvolvement', 'JobSatisfaction', 'MonthlyIncome',\n",
       "       'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',\n",
       "       'RelationshipSatisfaction', 'StockOptionLevel', 'TrainingTimesLastYear',\n",
       "       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n",
       "       'YearsSinceLastPromotion', 'YearsWithCurrManager', 'bt_Non-Travel',\n",
       "       'bt_Travel_Frequently', 'bt_Travel_Rarely', 'd_Human Resources',\n",
       "       'd_Research & Development', 'd_Sales', 'ef_Human Resources',\n",
       "       'ef_Life Sciences', 'ef_Marketing', 'ef_Medical', 'ef_Other',\n",
       "       'ef_Technical Degree', 'j_Healthcare Representative',\n",
       "       'j_Human Resources', 'j_Laboratory Technician', 'j_Manager',\n",
       "       'j_Manufacturing Director', 'j_Research Director',\n",
       "       'j_Research Scientist', 'j_Sales Executive', 'j_Sales Representative',\n",
       "       'ms_Divorced', 'ms_Married', 'ms_Single'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'DistanceFromHome', 'Education', 'EnvironmentSatisfaction',\n",
       "       'Gender', 'JobInvolvement', 'JobSatisfaction', 'MonthlyIncome',\n",
       "       'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',\n",
       "       'RelationshipSatisfaction', 'StockOptionLevel', 'TrainingTimesLastYear',\n",
       "       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n",
       "       'YearsSinceLastPromotion', 'YearsWithCurrManager', 'bt_Non-Travel',\n",
       "       'bt_Travel_Frequently', 'bt_Travel_Rarely', 'd_Human Resources',\n",
       "       'd_Research & Development', 'd_Sales', 'ef_Human Resources',\n",
       "       'ef_Life Sciences', 'ef_Marketing', 'ef_Medical', 'ef_Other',\n",
       "       'ef_Technical Degree', 'j_Healthcare Representative',\n",
       "       'j_Human Resources', 'j_Laboratory Technician', 'j_Manager',\n",
       "       'j_Manufacturing Director', 'j_Research Director',\n",
       "       'j_Research Scientist', 'j_Sales Executive', 'j_Sales Representative',\n",
       "       'ms_Divorced', 'ms_Married', 'ms_Single'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##trans\n",
    "test_data[['OverTime','Gender']] =test_data[['OverTime','Gender']].applymap(str).applymap(convertBool2Int)  \n",
    "test_BusinessTravel = pd.get_dummies(test_data.BusinessTravel,prefix='bt') \n",
    "test_Department = pd.get_dummies(test_data.Department,prefix='d') \n",
    "test_EducationField = pd.get_dummies(test_data.EducationField,prefix='ef') \n",
    "test_JobRole= pd.get_dummies(test_data.JobRole,prefix='j') \n",
    "test_MaritalStatus= pd.get_dummies(test_data.MaritalStatus,prefix='ms') \n",
    "testData = pd.concat([test_data[features],test_BusinessTravel,test_Department, test_EducationField,test_JobRole,test_MaritalStatus], axis = 1)\n",
    "testData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "var =[ 'bt_Non-Travel','bt_Travel_Frequently', 'bt_Travel_Rarely', 'd_Human Resources',\n",
    "       'd_Research & Development', 'd_Sales', 'ef_Human Resources',\n",
    "       'ef_Life Sciences', 'ef_Marketing', 'ef_Medical', 'ef_Other',\n",
    "       'ef_Technical Degree', 'j_Healthcare Representative',\n",
    "       'j_Human Resources', 'j_Laboratory Technician', 'j_Manager',\n",
    "       'j_Manufacturing Director', 'j_Research Director',\n",
    "       'j_Research Scientist', 'j_Sales Executive', 'j_Sales Representative',\n",
    "       'ms_Divorced', 'ms_Married', 'ms_Single']\n",
    "trainData.drop(['bt_Travel_Rarely','d_Human Resources', 'ef_Other','j_Sales Representative','ms_Single'],axis=1,inplace=True)\n",
    "testData.drop(['bt_Travel_Rarely','d_Human Resources', 'ef_Other','j_Sales Representative','ms_Single'],axis=1,inplace=True)\n",
    "##0.905714 准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.归一化后用LR模型，val好于未归一化的，test为（0.888571）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(trainData)\n",
    "trainData_scale = scaler.transform(trainData) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "testData_scale = scaler.transform(testData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8863636363636364"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_val,y_train,y_val =train_test_split(trainData_scale, target,test_size=0.2)\n",
    "clf_scale = LogisticRegression(random_state=0,penalty='l1' )\n",
    "# clf_scale.fit(trainData_scale, target)\n",
    "clf_scale.fit(X_train, y_train)\n",
    "clf_scale.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                0       abs\n",
      "OverTime                 1.881539  1.881539\n",
      "Age                     -1.678629  1.678629\n",
      "YearsInCurrentRole      -1.438085  1.438085\n",
      "YearsSinceLastPromotion  1.276547  1.276547\n",
      "JobSatisfaction         -1.196782  1.196782\n"
     ]
    }
   ],
   "source": [
    "df_coef = pd.DataFrame(index=trainData.columns,data=np.transpose(clf_scale.coef_))\n",
    "df_coef['abs'] = df_coef.iloc[:,0].abs()\n",
    "df_coef = df_coef.sort_values(by='abs', ascending=False)\n",
    "print(df_coef.head(5)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = clf_scale.predict(testData_scale) \n",
    "df_test = pd.DataFrame(y_test,columns=['result'])\n",
    "df_test.to_csv(\"sample_scale_drop.csv\",index=0,header=1,columns=['result'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.RFE选择特征后用Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(trainData)\n",
    "trainData_scale = scaler.transform(trainData) \n",
    "testData_scale = scaler.transform(testData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True False  True  True  True  True  True  True  True False  True\n",
      "  True  True  True False  True  True  True  True  True False  True  True\n",
      " False  True False  True  True  True  True False  True  True False  True\n",
      "  True  True]\n",
      "[1 1 4 1 1 1 1 1 1 1 2 1 1 1 1 8 1 1 1 1 1 6 1 1 5 1 9 1 1 1 1 3 1 1 7 1 1\n",
      " 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "rfe = RFE(model, 30)\n",
    "rfe = rfe.fit(trainData_scale, target)\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1100, 38)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainData_scale.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.75      ],\n",
       "       [0.        , 0.75      ],\n",
       "       [0.21428571, 0.5       ],\n",
       "       ...,\n",
       "       [0.53571429, 0.75      ],\n",
       "       [0.21428571, 0.        ],\n",
       "       [0.03571429, 0.5       ]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainData_scale[:,[1,2]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#选择rfe.support_是true的数据\n",
    "features_var =[0,1,3,4,5,6,7,8,11,12,13,14,16,17,18,19,20,22,23,25,27,28,29,30,32,33,35,36,37]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.818\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "trainDataSe =trainData_scale[:,[0,1,3,4,5,6,7,8,11,12,13,14,16,17,18,19,20,22,23,25,27,28,29,30,32,33,35,36,37]] \n",
    "X_train,X_val,y_train,y_val =train_test_split(trainDataSe, target,test_size=0.2)\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "print('Random Forest Accuracy: {:.3f}'.format(accuracy_score(y_val, rf.predict(X_val))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "testDataSe =testData_scale[:,[0,1,3,4,5,6,7,8,11,12,13,14,16,17,18,19,20,22,23,25,27,28,29,30,32,33,35,36,37]] \n",
    "y_test = rf.predict(testDataSe) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.DataFrame(y_test,columns=['result'])\n",
    "df_test.to_csv(\"sample_rf.csv\",index=0,header=1,columns=['result'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.直接LR模型,val表现不是太好，但是test为（0.874286）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8454545454545455"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_val,y_train,y_val =train_test_split(trainData, target,test_size=0.2)\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs' )\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = clf.predict(testData_scale) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.DataFrame(y_test,columns=['result'])\n",
    "df_test.to_csv(\"sample_noscale.csv\",index=0,header=1,columns=['result'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.pca降维后用LR模型，降维后没有看到变化，没提交"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.降维效果不太好，特征数据比较分散，没有几个特征贡献率和很高的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.15355537 0.08639357 0.07624677 0.05935454 0.05107532 0.04948934\n",
      " 0.04497724 0.03762264 0.03604883 0.03392498 0.03264719 0.03082399\n",
      " 0.0290741  0.02514667 0.02346541 0.02206755 0.02117212 0.01979623\n",
      " 0.01947542 0.01803989 0.01577304 0.01468961 0.01341297 0.01312934\n",
      " 0.01253725 0.01220911 0.01001738 0.00782542 0.00686035 0.00602085]\n",
      "pca reserved percent= 0.9828724798388502\n",
      "train_val= 0.8545454545454545\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=30)\n",
    "pca.fit(trainData_scale)\n",
    "X_pca=pca.transform(trainData_scale)\n",
    "print(pca.explained_variance_ratio_)  \n",
    "print(\"pca reserved percent=\",pca.explained_variance_ratio_.sum())  \n",
    "X_train, X_val, y_train, y_val = train_test_split(X_pca, target, test_size=0.2, random_state=42)\n",
    "reg_pca = LogisticRegression().fit(X_train, y_train) \n",
    "print(\"train_val=\",reg_pca.score(X_val,y_val) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = testData\n",
    "X_test_pca=pca.transform(testData_scale)\n",
    "y_test=reg_pca.predict(X_test_pca) \n",
    "df = pd.DataFrame (y_test, columns = ['result'])\n",
    "df.to_csv(\"sample_pca.csv\",encoding = \"utf-8\",header=1,index=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.考虑gbdt进行特征选择后用LR模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_lr, X_1, y_train_lr, y_1 = train_test_split(trainData, target, test_size=0.5, random_state=42)\n",
    "X_gbdt,X_val, y_gbdt,y_val = train_test_split(X_1, y_1, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.linear_model import LogisticRegression  \n",
    "def GBDTLR():\n",
    "    GBDT = GradientBoostingClassifier(n_estimators=10)\n",
    "    GBDT.fit(X_gbdt, y_gbdt)\n",
    "    OHE = OneHotEncoder()\n",
    "    OHE.fit(GBDT.apply(X_gbdt)[:, :, 0])\n",
    "    LR = LogisticRegression()\n",
    "    LR.fit(OHE.transform(GBDT.apply(X_train_lr)[:, :, 0]), y_train_lr)\n",
    "    #test\n",
    "    X_t_trans = OHE.transform(GBDT.apply(X_val)[:, :, 0])\n",
    "    auc = LR.score(X_t_trans, y_val)\n",
    "    print('GradientBoosting + LogisticRegression: ', auc)\n",
    "    X_test_trans = OHE.transform(GBDT.apply(testData)[:, :, 0])\n",
    "    Y_test = LR.predict(X_test_trans)\n",
    "    df = pd.DataFrame (Y_test, columns = ['result'])\n",
    "    df.to_csv(\"sample_gbdtlr.csv\",encoding = \"utf-8\",header=1,index=0)\n",
    "#     return Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoosting + LogisticRegression:  0.7636363636363637\n"
     ]
    }
   ],
   "source": [
    "GBDTLR()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.直接用xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "X_train, X_val, y_train, y_val = train_test_split(trainData, target, test_size=0.7, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoostTest():\n",
    "    XGBmodel = xgb.XGBClassifier(nthread=4, learning_rate=0.08, n_estimators=100,colsample_bytree=0.85, max_depth =6)\n",
    "#     XGBmodel.fit(trainData, target)\n",
    "    XGBmodel.fit(X_train, y_train)\n",
    "#     y_pred = XGB.predict(X_val) \n",
    "    auc = XGBmodel.score(X_val,y_val)\n",
    "    print('XGBoost: ', auc) \n",
    "    Y_test = XGBmodel.predict(testData)\n",
    "    df = pd.DataFrame (Y_test, columns = ['result'])\n",
    "    df.to_csv(\"sample_xgb.csv\",encoding = \"utf-8\",header=1,index=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost:  0.8675324675324675\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\IDE\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "D:\\IDE\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "XGBoostTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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